Nowadays, more and more users keep up with news through information streams coming from real-time micro-blogging activity offered by services such as Twitter. In these sites, information is shared via a followers/followees social network structure in which a follower will receive all the micro-blogs from the users he follows, named followees. Recent research efforts on understanding micro-blogging as a novel form of communication and news spreading medium have identified different categories of users in Twitter: information sources, information seekers and friends. Users acting as information sources are characterized for having a larger number of followers than followees, information seekers subscribe to this kind of users but rarely post tweets and, finally, friends are users exhibiting reciprocal relationships. With information seekers being an important portion of registered users in the system, finding relevant and reliable sources becomes essential. To address this problem, we propose a followee recommender system based on an algorithm that explores the topology of followers/followees network of Twitter considering different factors that allow us to identify users as good information sources. Experimental evaluation conducted with a group of users is reported, demonstrating the potential of the approach.